Merge pull request #3926 from pipecat-ai/mb/update-deps-2026-03-05

Update dependency version ranges for flexibility

examples/foundational/07-interruptible-cartesia-http.py

@@ -6,6 +6,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -52,60 +53,64 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    async with aiohttp.ClientSession() as session:
+        stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
 
-    tts = CartesiaHttpTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            aiohttp_session=session,
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-    )
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        )
 
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
+        context = LLMContext()
+        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        )
 
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            context.add_message(
+                {"role": "system", "content": "Please introduce yourself to the user."}
+            )
+            await task.queue_frames([LLMRunFrame()])
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+            await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

examples/foundational/07c-interruptible-deepgram-sagemaker.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/foundational/07w-interruptible-fal.py

@@ -7,6 +7,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -53,62 +54,66 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = FalSTTService(
-        api_key=os.getenv("FAL_KEY"),
-    )
+    async with aiohttp.ClientSession() as session:
+        stt = FalSTTService(
+            api_key=os.getenv("FAL_KEY"),
+            aiohttp_session=session,
+        )
 
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-    )
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        )
 
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
+        context = LLMContext()
+        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        )
 
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            context.add_message(
+                {"role": "system", "content": "Please introduce yourself to the user."}
+            )
+            await task.queue_frames([LLMRunFrame()])
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+            await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

pyproject.toml

@@ -20,10 +20,10 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence"
 ]
 dependencies = [
-    "aiofiles>=24.1.0,<25",
+    "aiofiles>=24.1.0,<27",
     "aiohttp>=3.11.12,<4",
     "audioop-lts~=0.2.1; python_version>='3.13'",
-    "docstring_parser~=0.16",
+    "docstring_parser>=0.16,<1",
     "loguru~=0.7.3",
     "Markdown>=3.7,<4",
     "nltk>=3.9.3,<4",
@@ -31,16 +31,16 @@ dependencies = [
     "Pillow>=11.1.0,<13",
     "protobuf~=5.29.6",
     "pydantic>=2.10.6,<3",
-    "pyloudnorm~=0.1.1",
+    "pyloudnorm~=0.2.0",
     "resampy~=0.4.3",
-    "soxr~=0.5.0",
+    "soxr~=1.0.0",
     "openai>=1.74.0,<3",
     # Pinning numba to resolve package dependencies
-    "numba>=0.61.2",
-    "wait_for2>=0.4.1; python_version<'3.12'",
+    "numba>=0.61.2,<1",
+    "wait_for2>=0.4.1,<1; python_version<'3.12'",
     # Required by LocalSmartTurnAnalyzerV3
     # Inlined here instead of using a self-referential extra for Poetry compatibility.
-    "transformers",
+    "transformers>=4.48.0,<6",
     "onnxruntime~=1.23.2",
 ]
 
@@ -53,48 +53,48 @@ Changelog = "https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md"
 
 [project.optional-dependencies]
 aic = [ "aic-sdk~=2.1.0" ]
-anthropic = [ "anthropic~=0.49.0" ]
+anthropic = [ "anthropic>=0.49.0,<1" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
-aws = [ "aioboto3~=15.5.0", "pipecat-ai[websockets-base]" ]
-aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.2.0; python_version>='3.12'" ]
-azure = [ "azure-cognitiveservices-speech~=1.47.0"]
-cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
-camb = [ "camb-sdk>=1.5.4" ]
+aws = [ "aioboto3>=15.5.0,<16", "pipecat-ai[websockets-base]" ]
+aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.4.0; python_version>='3.12'" ]
+azure = [ "azure-cognitiveservices-speech>=1.47.0,<2"]
+cartesia = [ "pipecat-ai[websockets-base]" ]
+camb = [ "camb-sdk>=1.5.4,<2" ]
 cerebras = []
 daily = [ "daily-python~=0.23.0" ]
-deepgram = [ "deepgram-sdk~=6.0.1", "pipecat-ai[websockets-base]" ]
+deepgram = [ "deepgram-sdk>=6.0.1,<7", "pipecat-ai[websockets-base]" ]
 deepseek = []
 elevenlabs = [ "pipecat-ai[websockets-base]" ]
-fal = [ "fal-client~=0.5.9" ]
+fal = []
 fireworks = []
-fish = [ "ormsgpack~=1.7.0", "pipecat-ai[websockets-base]" ]
+fish = [ "ormsgpack>=1.7.0,<2", "pipecat-ai[websockets-base]" ]
 gladia = [ "pipecat-ai[websockets-base]" ]
 google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.57.0,<2", "pipecat-ai[websockets-base]" ]
 gradium = [ "pipecat-ai[websockets-base]" ]
 grok = []
-groq = [ "groq~=0.23.0" ]
+groq = [ "groq>=0.23.0,<2" ]
 gstreamer = [ "pygobject~=3.50.0" ]
-heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ]
-hume = [ "hume>=0.11.2" ]
+heygen = [ "livekit>=1.0.13,<2", "pipecat-ai[websockets-base]" ]
+hume = [ "hume>=0.11.2,<1" ]
 inworld = []
 koala = [ "pvkoala~=2.0.3" ]
 kokoro = [ "kokoro-onnx>=0.5.0,<1", "requests>=2.32.5,<3" ]
 krisp = [ "pipecat-ai-krisp~=0.4.0" ]
 langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ]
 lemonslice = [ "pipecat-ai[daily]" ]
-livekit = [ "livekit~=1.0.13", "livekit-api~=1.0.5", "tenacity>=8.2.3,<10.0.0", "pyjwt>=2.10.1" ]
+livekit = [ "livekit>=1.0.13,<2", "livekit-api>=1.0.5,<2", "tenacity>=8.2.3,<10.0.0", "pyjwt>=2.10.1,<3" ]
 lmnt = [ "pipecat-ai[websockets-base]" ]
 local = [ "pyaudio~=0.2.14" ]
-local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
+local-smart-turn = [ "coremltools>=8.0", "transformers>=4.48.0,<6", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
 mcp = [ "mcp[cli]>=1.11.0,<2" ]
 mem0 = [ "mem0ai~=0.1.94" ]
 mistral = []
 mlx-whisper = [ "mlx-whisper~=0.4.2" ]
-moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0" ]
+moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0,<6" ]
 neuphonic = [ "pipecat-ai[websockets-base]" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
-nvidia = [ "nvidia-riva-client~=2.21.1" ]
+nvidia = [ "nvidia-riva-client>=2.21.1,<3" ]
 openai = [ "pipecat-ai[websockets-base]" ]
 rnnoise = [ "pyrnnoise~=0.4.1" ]
 openpipe = [ "openpipe>=4.50.0,<6" ]
@@ -106,12 +106,12 @@ remote-smart-turn = []
 resembleai = [ "pipecat-ai[websockets-base]" ]
 rime = [ "pipecat-ai[websockets-base]" ]
 riva = [ "pipecat-ai[nvidia]" ]
-runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.128.0", "pipecat-ai-small-webrtc-prebuilt>=2.3.0"]
+runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<1", "pipecat-ai-small-webrtc-prebuilt>=2.3.0"]
 sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"]
 sambanova = []
 sarvam = [ "sarvamai==0.1.26a2", "pipecat-ai[websockets-base]" ]
 sentry = [ "sentry-sdk>=2.28.0,<3" ]
-silero = [ "onnxruntime~=1.23.2" ]
+silero = []
 simli = [ "simli-ai~=2.0.1"]
 soniox = [ "pipecat-ai[websockets-base]" ]
 soundfile = [ "soundfile~=0.13.1" ]
@@ -119,20 +119,20 @@ speechmatics = [ "speechmatics-voice[smart]~=0.2.8" ]
 strands = [ "strands-agents>=1.9.1,<2" ]
 tavus=[]
 together = []
-tracing = [ "opentelemetry-sdk>=1.33.0", "opentelemetry-api>=1.33.0", "opentelemetry-instrumentation>=0.54b0" ]
+tracing = [ "opentelemetry-sdk>=1.33.0,<2", "opentelemetry-api>=1.33.0,<2", "opentelemetry-instrumentation>=0.54b0,<1" ]
 ultravox = [ "pipecat-ai[websockets-base]" ]
 webrtc = [ "aiortc>=1.14.0,<2", "opencv-python>=4.11.0.86,<5" ]
-websocket = [ "pipecat-ai[websockets-base]", "fastapi>=0.115.6,<0.128.0" ]
+websocket = [ "pipecat-ai[websockets-base]", "fastapi>=0.115.6,<1" ]
 websockets-base = [ "websockets>=13.1,<16.0" ]
-whisper = [ "faster-whisper~=1.1.1" ]
+whisper = [ "faster-whisper~=1.2.1" ]
 
 [dependency-groups]
 dev = [
-    "build~=1.2.2",
-    "coverage~=7.9.1",
+    "build~=1.4.0",
+    "coverage~=7.13.4",
     "grpcio-tools~=1.67.1",
-    "pip-tools~=7.4.1",
-    "pre-commit~=4.2.0",
+    "pip-tools~=7.5.3",
+    "pre-commit~=4.5.1",
     "pyright>=1.1.404,<1.2",
     "pytest~=8.4.1",
     "pytest-asyncio~=1.3.0",

src/pipecat/audio/vad/silero.py

@@ -27,7 +27,7 @@ try:
 
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
-    logger.error("In order to use Silero VAD, you need to `pip install pipecat-ai[silero]`.")
+    logger.error("In order to use Silero VAD, you need to `pip install pipecat-ai`.")
     raise Exception(f"Missing module(s): {e}")
 
 

src/pipecat/services/cartesia/tts.py

@@ -13,6 +13,7 @@ from dataclasses import dataclass, field
 from enum import Enum
 from typing import Any, AsyncGenerator, List, Literal, Mapping, Optional
 
+import aiohttp
 from loguru import logger
 from pydantic import BaseModel, Field
 
@@ -33,13 +34,8 @@ from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
 from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
 from pipecat.utils.tracing.service_decorators import traced_tts
 
-# Suppress regex warnings from pydub (used by cartesia)
-warnings.filterwarnings("ignore", message="invalid escape sequence", category=SyntaxWarning)
-
-
 # See .env.example for Cartesia configuration needed
 try:
-    from cartesia import AsyncCartesia
     from websockets.asyncio.client import connect as websocket_connect
     from websockets.protocol import State
 except ModuleNotFoundError as e:
@@ -721,6 +717,7 @@ class CartesiaHttpTTSService(TTSService):
         model: str = "sonic-3",
         base_url: str = "https://api.cartesia.ai",
         cartesia_version: str = "2024-11-13",
+        aiohttp_session: Optional[aiohttp.ClientSession] = None,
         sample_rate: Optional[int] = None,
         encoding: str = "pcm_s16le",
         container: str = "raw",
@@ -735,6 +732,8 @@ class CartesiaHttpTTSService(TTSService):
             model: TTS model to use (e.g., "sonic-3").
             base_url: Base URL for Cartesia HTTP API.
             cartesia_version: API version string for Cartesia service.
+            aiohttp_session: Optional aiohttp ClientSession for HTTP requests.
+                If not provided, a session will be created and managed internally.
             sample_rate: Audio sample rate. If None, uses default.
             encoding: Audio encoding format.
             container: Audio container format.
@@ -766,10 +765,8 @@ class CartesiaHttpTTSService(TTSService):
         self._base_url = base_url
         self._cartesia_version = cartesia_version
 
-        self._client = AsyncCartesia(
-            api_key=api_key,
-            base_url=base_url,
-        )
+        self._session: aiohttp.ClientSession | None = aiohttp_session
+        self._owns_session = aiohttp_session is None
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -798,6 +795,14 @@ class CartesiaHttpTTSService(TTSService):
         """
         await super().start(frame)
         self._settings.output_sample_rate = self.sample_rate
+        if self._owns_session:
+            self._session = aiohttp.ClientSession()
+
+    async def _close_session(self):
+        """Close the HTTP session if we own it."""
+        if self._owns_session and self._session:
+            await self._session.close()
+            self._session = None
 
     async def stop(self, frame: EndFrame):
         """Stop the Cartesia HTTP TTS service.
@@ -806,7 +811,7 @@ class CartesiaHttpTTSService(TTSService):
             frame: The end frame.
         """
         await super().stop(frame)
-        await self._client.close()
+        await self._close_session()
 
     async def cancel(self, frame: CancelFrame):
         """Cancel the Cartesia HTTP TTS service.
@@ -815,7 +820,7 @@ class CartesiaHttpTTSService(TTSService):
             frame: The cancel frame.
         """
         await super().cancel(frame)
-        await self._client.close()
+        await self._close_session()
 
     @traced_tts
     async def run_tts(self, text: str, context_id: str) -> AsyncGenerator[Frame, None]:
@@ -874,8 +879,6 @@ class CartesiaHttpTTSService(TTSService):
 
             yield TTSStartedFrame(context_id=context_id)
 
-            session = await self._client._get_session()
-
             headers = {
                 "Cartesia-Version": self._cartesia_version,
                 "X-API-Key": self._api_key,
@@ -884,7 +887,7 @@ class CartesiaHttpTTSService(TTSService):
 
             url = f"{self._base_url}/tts/bytes"
 
-            async with session.post(url, json=payload, headers=headers) as response:
+            async with self._session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
                     yield ErrorFrame(error=f"Cartesia API error: {error_text}")

src/pipecat/services/fal/image.py

@@ -25,13 +25,6 @@ from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame
 from pipecat.services.image_service import ImageGenService
 from pipecat.services.settings import ImageGenSettings
 
-try:
-    import fal_client
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use Fal, you need to `pip install pipecat-ai[fal]`.")
-    raise Exception(f"Missing module: {e}")
-
 
 @dataclass
 class FalImageGenSettings(ImageGenSettings):
@@ -91,6 +84,7 @@ class FalImageGenService(ImageGenService):
         super().__init__(settings=FalImageGenSettings(model=model), **kwargs)
         self._params = params
         self._aiohttp_session = aiohttp_session
+        self._api_key = key or os.getenv("FAL_KEY", "")
         if key:
             os.environ["FAL_KEY"] = key
 
@@ -112,10 +106,22 @@ class FalImageGenService(ImageGenService):
 
         logger.debug(f"Generating image from prompt: {prompt}")
 
-        response = await fal_client.run_async(
-            self._settings.model,
-            arguments={"prompt": prompt, **self._params.model_dump(exclude_none=True)},
-        )
+        headers = {
+            "Authorization": f"Key {self._api_key}",
+            "Content-Type": "application/json",
+        }
+        payload = {"prompt": prompt, **self._params.model_dump(exclude_none=True)}
+
+        async with self._aiohttp_session.post(
+            f"https://fal.run/{self._settings.model}",
+            json=payload,
+            headers=headers,
+        ) as resp:
+            if resp.status != 200:
+                error_text = await resp.text()
+                yield ErrorFrame(error=f"Fal API error ({resp.status}): {error_text}")
+                return
+            response = await resp.json()
 
         image_url = response["images"][0]["url"] if response else None
 

src/pipecat/services/fal/stt.py

@@ -10,10 +10,12 @@ This module provides integration with Fal's Wizper API for speech-to-text
 transcription using segmented audio processing.
 """
 
+import base64
 import os
 from dataclasses import dataclass, field
-from typing import Any, AsyncGenerator, Optional
+from typing import AsyncGenerator, Optional
 
+import aiohttp
 from loguru import logger
 from pydantic import BaseModel
 
@@ -25,15 +27,6 @@ from pipecat.transcriptions.language import Language, resolve_language
 from pipecat.utils.time import time_now_iso8601
 from pipecat.utils.tracing.service_decorators import traced_stt
 
-try:
-    import fal_client
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use Fal, you need to `pip install pipecat-ai[fal]`. Also, set `FAL_KEY` environment variable."
-    )
-    raise Exception(f"Missing module: {e}")
-
 
 def language_to_fal_language(language: Language) -> Optional[str]:
     """Convert a Language enum to Fal's Wizper language code.
@@ -192,6 +185,7 @@ class FalSTTService(SegmentedSTTService):
         self,
         *,
         api_key: Optional[str] = None,
+        aiohttp_session: Optional[aiohttp.ClientSession] = None,
         sample_rate: Optional[int] = None,
         params: Optional[InputParams] = None,
         ttfs_p99_latency: Optional[float] = FAL_TTFS_P99,
@@ -201,6 +195,8 @@ class FalSTTService(SegmentedSTTService):
 
         Args:
             api_key: Fal API key. If not provided, will check FAL_KEY environment variable.
+            aiohttp_session: Optional aiohttp ClientSession for HTTP requests.
+                If not provided, a session will be created and managed internally.
             sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate.
             params: Configuration parameters for the Wizper API.
             ttfs_p99_latency: P99 latency from speech end to final transcript in seconds.
@@ -224,14 +220,14 @@ class FalSTTService(SegmentedSTTService):
             **kwargs,
         )
 
-        if api_key:
-            os.environ["FAL_KEY"] = api_key
-        elif "FAL_KEY" not in os.environ:
+        self._api_key = api_key or os.getenv("FAL_KEY", "")
+        if not self._api_key:
             raise ValueError(
                 "FAL_KEY must be provided either through api_key parameter or environment variable"
             )
 
-        self._fal_client = fal_client.AsyncClient(key=api_key or os.getenv("FAL_KEY"))
+        self._session: aiohttp.ClientSession | None = aiohttp_session
+        self._owns_session = aiohttp_session is None
 
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate processing metrics.
@@ -275,12 +271,26 @@ class FalSTTService(SegmentedSTTService):
         try:
             await self.start_processing_metrics()
 
-            # Send to Fal directly (audio is already in WAV format from base class)
-            data_uri = fal_client.encode(audio, "audio/x-wav")
-            response = await self._fal_client.run(
-                "fal-ai/wizper",
-                arguments={"audio_url": data_uri, **self._settings.given_fields()},
-            )
+            if not self._session:
+                self._session = aiohttp.ClientSession()
+
+            data_uri = f"data:audio/x-wav;base64,{base64.b64encode(audio).decode()}"
+            payload = {"audio_url": data_uri, **self._settings.given_fields()}
+            headers = {
+                "Authorization": f"Key {self._api_key}",
+                "Content-Type": "application/json",
+            }
+
+            async with self._session.post(
+                "https://fal.run/fal-ai/wizper",
+                json=payload,
+                headers=headers,
+            ) as resp:
+                if resp.status != 200:
+                    error_text = await resp.text()
+                    yield ErrorFrame(error=f"Fal API error ({resp.status}): {error_text}")
+                    return
+                response = await resp.json()
 
             if response and "text" in response:
                 text = response["text"].strip()

uv.lock

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+    { url = "https://files.pythonhosted.org/packages/ef/cc/95cc6a61885dcc2cbc34167bed9cfd3183cf0a0a79b0e06388d8f4a78b86/aws_sdk_sagemaker_runtime_http2-0.4.0-py3-none-any.whl", hash = "sha256:be1e3415047058a655f682cb97f2ba7cadd46a30100bb8f2ef1b1c2c8d41204b", size = 21802, upload-time = "2026-02-24T18:55:27.804Z" },
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 [[package]]
@@ -640,7 +640,7 @@ wheels = [
 
 [[package]]
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-version = "1.2.2.post1"
+version = "1.4.0"
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 dependencies = [
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@@ -649,9 +649,9 @@ dependencies = [
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-    { name = "iterators" },
-    { name = "pydantic" },
-    { name = "pydantic-core" },
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 version = "25.2.0"
@@ -1118,66 +1097,115 @@ wheels = [
 
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